Gender: Female 
Age: 18-24
Nationality: United Kingdom
Ethnicity: Black or African American 
Marital status: Single 
First language: English 
Religion: Nothing 
Education: Undergraduate degree
Mother's education: Graduate degree
Father's education: Doctorate or above
Self-assessed socioeconomic status (1-10): 4
Home: Rent 
Employment: Student
Occupation: Prefer not to say
Mother's occupation: Prefer not to say
Father's occupation: Prefer not to say
Hobbies: Listen to hiphop and rap;Use social media;Do arts and crafts;Watch sports;Listen to rock/indie music 
Devices used daily: Laptop;Smartphone 
NLP tools known: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Dialog technology (e.g. chatbots that communicate with you such as chatGPT);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language) 
NLP tools used: Grammar checker (i.e., correcting grammar mistakes);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text) 
NLP tools they would find useful: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa);Text-to-Speech (i.e, computers being able to read out loud written language);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Dialog technology (e.g. chatbots that communicate with you such as chatGPT) 
Frequency of LLM use: Sometimes
LLMs used: ChatGPT;GitHub Copilot;Google Gemini;Microsoft Bing AI;Snapchat My AI 
LLM use cases: Coding;Brainstorming;Writing (e.g., composing emails);Paraphrasing or finding synonyms;Proofreading/Editing (e.g., grammar, spelling, structure improvements);Generic chatbot conversation;Solving math or logical problems;Completing assignments;Answering questions about general knowledge;Summarizing long texts;Learning (e.g., explaining complex concepts in simple terms);Collecting references 
Contexts of LLM use: School/University;Entertainment;Learning;Personal 
Prompt 1: Consider steady two-dimensional heat transfer in a long solid body whose cross section is given in the figure. The temperature at the selected nodes and the thermal conditions at the boundaries are shown. The thermal conductivity of the body k= 45 W/mK, and heat is generated in the body uniformly at a rate of 𝑒̇ = 4 × 106𝑊/𝑚3. Using finite difference method with a mesh size of x=y=4 cm, determine (a) the temperature at nodes 1, 2, and 3 and (b) the rate of heat loss from the bottom surface through a 1 m long section of the body